ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING IN DRUG DISCOVERY, REPURPOSING, AND CLINICAL TRIAL OPTIMIZATION

Artificial intelligence and machine learning are transforming pharmaceutical research by enabling faster, cheaper, and more informed decision-making across the drug development pipeline. This review examines the applications of AI and machine learning in three major domains: drug discovery, drug repurposing, and clinical trial optimization. Deep learning architectures, including convolutional neural networks, graph neural networks, transformers, and generative models, have been applied to molecular property prediction, de novo drug design, and virtual screening. AlphaFold2 has revolutionized protein structure prediction, providing new opportunities for structure-based drug discovery. Machine learning has also accelerated drug repurposing efforts, particularly during the COVID-19 pandemic, by identifying candidate compounds from existing drug libraries. In clinical trials, AI tools are being used for patient recruitment, eligibility assessment, adaptive trial design, and synthetic control arms, with the potential to reduce cost and improve success rates. However, significant challenges remain, including data quality and availability, model interpretability, regulatory acceptance, and the risk of bias. This review critically discusses recent progress, limitations, and future directions, drawing primarily on literature published through early 2023. The evidence suggests that while AI has not yet produced a paradigm-shifting number of approved drugs, its influence is expanding rapidly and is likely to become integral to drug development.